As AI unleashes a host of new tools and agents to help engineers build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively.

François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing.

https://knowledgegraphinsights.com/francois-scharffe/

When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort.

Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering."

https://knowledgegraphinsights.com/jessica-2/

Knowledge graphs are complicated. They might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements.

Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture. A super domain might establish context that holds for just a few seconds for an ATM transaction or for several years for a clinical trial.

https://knowledgegraphinsights.com/eric-little/